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Add argument for CNN maxout pieces

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Matthew Honnibal 2017-09-20 19:14:41 -05:00
parent 78301b2d29
commit f5144f04be
1 changed files with 7 additions and 4 deletions
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11
spacy/_ml.py
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@ -226,9 +226,9 @@ def drop_layer(layer, factor=2.):
return model return model
def Tok2Vec(width, embed_size, pretrained_dims=0): def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
if pretrained_dims is None: assert pretrained_dims is not None
pretrained_dims = 0 cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH] cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}): with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm') norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
@ -244,7 +244,10 @@ def Tok2Vec(width, embed_size, pretrained_dims=0):
>> LN(Maxout(width, width*4, pieces=3)), column=5) >> LN(Maxout(width, width*4, pieces=3)), column=5)
) )
) )
convolution = Residual(ExtractWindow(nW=1) >> LN(Maxout(width, width*3, pieces=3))) convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
)
if pretrained_dims >= 1: if pretrained_dims >= 1:
embed = concatenate_lists(trained_vectors, SpacyVectors) embed = concatenate_lists(trained_vectors, SpacyVectors)